Hierarchical network structure as the source of power-law frequency spectra (state-trait continua) in living and non-living systems: how physical traits and personalities emerge from first principles in biophysics

What causes organisms to have different body plans and personalities? We address this question by looking at universal principles that govern the morphology and behavior of living systems. Living systems display a small-world network structure in which many smaller clusters are nested within fewer larger ones, producing a fractal-like structure with a power-law cluster size distribution. Their dynamics show similar qualities: the timeseries of inner message passing and overt behavior contain high frequencies or 'states' that are nested within lower frequencies or 'traits'. Here, we argue that the nested modular (power-law) dynamics of living systems results from their nested modular (power-law) network structure: organisms 'vertically encode' the deep spatiotemporal structure of their environments, so that high frequencies (states) are produced by many small clusters at the base of a nested-modular hierarchy and lower frequencies (traits) are produced by fewer larger clusters at its top. These include physical as well as behavioral traits. Nested-modular structure causes higher frequencies to be embedded in lower frequencies, producing power-law dynamics. Such dynamics satisfy the need for efficient energy dissipation through networks of coupled oscillators, which also governs the dynamics of non-living systems (e.g. earthquake dynamics, stock market fluctuations). Thus, we provide a single explanation for power-law frequency spectra in both living and non-living systems. If hierarchical structure indeed produces hierarchical dynamics, the development (e.g. during maturation) and collapse (e.g. during disease) of hierarchical structure should leave specific traces in power-law frequency spectra that may serve as early warning signs to system failure. The applications of this idea range from embryology and personality psychology to sociology, evolutionary biology and clinical medicine

How higher goals are constructed and collapse under stress: a hierarchical Bayesian control systems perspective

In this paper, we show that organisms can be modeled as hierarchical Bayesian control systems with small world and information bottleneck (bow-tie) network structure. Such systems combine hierarchical perception with hierarchical goal setting and hierarchical action control. We argue that hierarchical Bayesian control systems produce deep hierarchies of goal states, from which it follows that organisms must have some form of 'highest goals'. For all organisms, these involve internal (self) models, external (social) models and overarching (normative) models. We show that goal hierarchies tend to decompose in a top-down manner under severe and prolonged levels of stress. This produces behavior that favors short-term and self-referential goals over long term, social and/or normative goals. The collapse of goal hierarchies is universally accompanied by an increase in entropy (disorder) in control systems that can serve as an early warning sign for tipping points (disease or death of the organism). In humans, learning goal hierarchies corresponds to personality development (maturation). The failure of goal hierarchies to mature properly corresponds to personality deficits. A top-down collapse of such hierarchies under stress is identified as a common factor in all forms of episodic mental disorders (psychopathology). The paper concludes by discussing ways of testing these hypotheses empirically

Can we predict the direction of sudden shifts in symptoms?:Transdiagnostic implications from a complex systems perspective on psychopathology

Recently, there has been renewed interest in the application of assumptions from complex systems theory in the field of psychopathology. One assumption, with high clinical relevance, is that sudden transitions in symptoms may be anticipated by rising instability in the system, which can be detected with early warning signals (EWS). Empirical studies support the idea that this principle also applies to the field of psychopathology. The current manuscript discusses whether assumptions from complex systems theory can additionally be informative with respect to the specific symptom dimension in which such a transition will occur (e.g. whether a transition towards anxious, depressive or manic symptoms is most likely). From a complex systems perspective, both EWS measured in single symptom dynamics and network symptom dynamics at large are hypothesized to provide clues regarding the direction of the transition. Challenging research designs are needed to provide empirical validation of these hypotheses. These designs should be able to follow sudden transitions 'live' using frequent observations of symptoms within individuals and apply a transdiagnostic approach to psychopathology. If the assumptions proposed are supported by empirical studies then this will signify a large improvement in the possibility for personalized estimations of the course of psychiatric symptoms. Such information can be extremely useful for early intervention strategies aimed at preventing specific psychiatric problems

An integrated network model of psychotic symptoms

AbstractThe full body of research on the nature of psychosis and its determinants indicates that a considerable number of factors are relevant to the development of hallucinations, delusions, and other positive symptoms, ranging from neurodevelopmental parameters and altered connectivity of brain regions to impaired cognitive functioning and social factors. We aimed to integrate these factors in a single mathematical model based on network theory. At the microscopic level this model explains positive symptoms of psychosis in terms of experiential equivalents of robust, high-frequency attractor states of neural networks. At the mesoscopic level it explains them in relation to global brain states, and at the macroscopic level in relation to social-network structures and dynamics. Due to the scale-free nature of biological networks, all three levels are governed by the same general laws, thereby allowing for an integrated model of biological, psychological, and social phenomena involved in the mediation of positive symptoms of psychosis. This integrated network model of psychotic symptoms (INMOPS) is described together with various possibilities for application in clinical practice

A Network View on Psychiatric Disorders: Network Clusters of Symptoms as Elementary Syndromes of Psychopathology

<div><p>Introduction</p><p>The vast number of psychopathological syndromes that can be observed in clinical practice can be described in terms of a limited number of elementary syndromes that are differentially expressed. Previous attempts to identify elementary syndromes have shown limitations that have slowed progress in the taxonomy of psychiatric disorders.</p><p>Aim</p><p>To examine the ability of network community detection (NCD) to identify elementary syndromes of psychopathology and move beyond the limitations of current classification methods in psychiatry.</p><p>Methods</p><p>192 patients with unselected mental disorders were tested on the Comprehensive Psychopathological Rating Scale (CPRS). Principal component analysis (PCA) was performed on the bootstrapped correlation matrix of symptom scores to extract the principal component structure (PCS). An undirected and weighted network graph was constructed from the same matrix. Network community structure (NCS) was optimized using a previously published technique.</p><p>Results</p><p>In the optimal network structure, network clusters showed a 89% match with principal components of psychopathology. Some 6 network clusters were found, including "DEPRESSION", "MANIA", “ANXIETY”, "PSYCHOSIS", "RETARDATION", and "BEHAVIORAL DISORGANIZATION". Network metrics were used to quantify the continuities between the elementary syndromes.</p><p>Conclusion</p><p>We present the first comprehensive network graph of psychopathology that is free from the biases of previous classifications: a ‘Psychopathology Web’. Clusters within this network represent elementary syndromes that are connected via a limited number of bridge symptoms. Many problems of previous classifications can be overcome by using a network approach to psychopathology.</p></div

Network metrics of individual symptoms of the Psychopathology Web.

<p>Item: item of the CPRS. Cluster: name of the network cluster to which the symptom belongs (one of 6 network clusters identified in the CPRS dataset). Bridge or core: specifies whether the symptom is a bridge symptom or a core symptom. Ext. clust.: number of external clusters that the (bridge) symptom connects with. Ln_w_D: logtransformed and weighted degree. Ln_w_BS: logtransformed and weighted betweenness centrality. Ln_w_CS: logtransformed and weighted closeness centrality.</p><p>Network metrics of individual symptoms of the Psychopathology Web.</p

Table showing the quantitative results of the cluster-to-component matching procedure.

<p>Table shows the network community structures (NCS) that were most similar to the confirmatory 5, 6, 7, 8, 9, and 10 principal component structure (PCS) of the CPRS dataset. Component structure: the component structure that was matched against the candidate network community structures obtained from the incremental pruning procedure (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112734#s2" target="_blank">Materials and Methods</a>). Principal Component: the number of the principal component from this component structure. % mismatch per cluster: the percentage of items in a network cluster of the most similar NCS that did not match the item content of its corresponding principal component. % overall mismatch: the percentage of items in the entire NCS that did not match its corresponding PCS. ABS(r): the absolute value of the correlation coefficient at which the optimal match with a NCS was found. p: the corresponding p value. Nrnodes: number of nodes left in the NCS at this threshold (some nodes dropped off the network due to incremental pruning, see text).</p><p>Table showing the quantitative results of the cluster-to-component matching procedure.</p

The Psychopathology Web.

<p>Network graph of the correlational relationships between 55 items (symptoms) of the CPRS, which form a 6-cluster structure. Node  =  CPRS item (symptom), link  =  significant correlation. The threshold for the significance of network links has been optimized using the procedure described above. Red links: positive correlations. Blue links: negative correlations. The thickness of the links reflects the strength of their corresponding correlation coefficient (weight). It01 etc: item number of the CPRS. Nodes are positioned according to the Hagel-Koren Fast Multiscale layout algorithm. The color of the nodes shows their network cluster membership. Yellow: ANXIETY, Light Blue: DEPRESSION. Orange: MANIA, Green: PSYCHOSIS, Grey: RETARDATION, Brown: BEHAVIORAL DISORGANIZATION. NCD and PCA differ with respect to the placement of items 03, 04, 25, 27, and 28. These mismatches occur at the boundaries between the DEPRESSION cluster and the PSYCHOSIS cluster, and between the DEPRESSION cluster and the ANXIETY cluster and can be interpreted as ‘border disputes’ between NCD and PCA. Spheres: bridge symptoms. Closed diamonds: core symptoms. Node size denotes betweenness centrality score of the node (a measure of its involvement in connecting the various parts of the Psychopathology Web through shortest paths). Smaller and larger loops can be observed that run within and between the various network clusters. See text for further details.</p

Scree-plot (A) and pruning plot (B) of the CPRS dataset.

<p>A. Scree-plot of the exploratory principal component analysis of the CPRS dataset, suggesting a 10-component structure. Defining a cut-off for the total number of components to extract was complicated by the lack of a clear bend in the plot. Hence, 5 additional alternative component structures were matched to the total array of possible network community structures of the dataset, to allow identification of an optimal solution. B. Incremental pruning plot of network community structure analyses showing the number of clusters in the Psychopathology Web as a function of the correlation coefficient that defines the threshold for significance of the links in the network. Neighborless nodes (isolates) are removed from the calculation and do not count as clusters.</p